Offline gain optimization in kinodynamic motion planning based on a harmonic potential field

A method of automatically optimizing gains is described for kinodynamic motion planning related to a controlled system consisting of a point mass. Kinodynamic motion planning proposed by Masoud has some gains and it is difficult to optimize such gains manually due to its interaction. Note, however, that any method for optimizing the gains has not been mentioned yet. Therefore, a method for optimizing all gains included in the kinodynamic motion planning is proposed by using a genetic algorithm.     

双层自适应快速super twisting控制算法

为提高super twising算法的收敛速度,解决现有算法存在的增益过估计问题,提出了两种自适应增益快速super twisting算法.分别通过快速终端滑模趋近律和增加线性项加快收敛速度.利用基于等效控制的双层自适应律调节增益,保证滑模存在条件的成立,同时使增益尽量的小.采用Lyapunov方法证明了新算法具有更优良的收敛特性,根据有界实引理和Schur补定理分别给出了两种算法的参数整定策略.仿真算例表明,在相同控制参数下,新算法的能耗与原算法接近,并具有更快的收敛速度和更强的鲁棒性.     

Decentralized stabilization of interconnected systems

An algorithm for the evaluation of the fixed modes of interconnected large-scale systems with respect to decentralized feedback gains is presented, as well as a procedure for the determination of decentralized stabilization and/or pole-placement feedback gains. The approach is based on eigenvalue dynamics, and closed-form expressions for eigenvalue sensitivities with respect to the decentralized feedback gains are obtained.     

Near-optimum regulator design of singularly perturbed systems via Chandrasekhar equations

The singular perturbation theory provides a powerful design and analysis approach for model order reduction. Another method of simplifying the calculation of feedback gains in an LQG problem is the Chandrasekhar algorithm, which replaces the usual Riccati approach. The method presented in this paper combines the advantages inherent in the singular perturbation theory with those of the Chandrasekhar algorithm. This is accomplished by using lower dimensional Chandrasekhar equations for calculating the asymptotic expansion terms of the feedback gains. Two alternative design procedures are given. In one procedure the fast or boundary layer gains are calculated first followed by the slow or reduced gains. In the other procedure a more conventional design sequence is adopted. That is, first the reduced problem is solved and the boundary layer correction terms are added later on if needed.     

基于稳态非线性模型和线性ARX模型组合的非线性预测控制

通过点集映射来表示非线性系统的稳态模型,用系统的稳态增益来修正具有外界输入的线性自回归（AutoRegressive with eXternal input, ARX）模型的动态增益,提出了一种基于稳态非线性模型和线性ARX模型组合的非线性预测控制算法．该算法用递归最小二乘法在线辨识系统的动态模型参数,用序列二次规划算法求解目标函数．最后通过对典型化工非线性对象pH中和过程的仿真对本算法进行了验证．结果表明,本算法比广义预测控制算法具有更好的设定值跟踪性能和抗干扰能力．     

A comparative design and tuning for conventional fuzzy control

A new methodology is introduced for designing and tuning the scaling gains of the conventional fuzzy logic controller (FLC) based on its well-tuned linear counterpart. The conventional FLC with a linear rule base is very similar to its linear counterpart. The linear three-term controller has proportional, integral and/or derivative gains. Similarly, the conventional fuzzy three-term controller also has fuzzy proportional, integral and/or derivative gains. The new concept "fuzzy transfer function" is invented to connect these fuzzy gains with the corresponding scaling gains. The comparative gain design is presented by using the gains of the well-tuned linear counterpart as the initial fuzzy gains of the conventional FLC. Furthermore, the relationship between the scaling gains and the performance can be deduced to produce the comparative tuning algorithm, which can tune the scaling gains to their optimum by less trial and error. The performance comparison in the simulation demonstrates the viability of the new methodology.     

6阵元智能天线赋形方法性能测试研究

本文研究蜂窝小区6阵元智能天线在不同场景下的天线赋形增益,及不同智能天线赋形算法的性能。实验测试结果表明,对于6阵元智能天线,采用赋形比不采用赋形方式能得到3—5dB的增益,采用特征分解赋性算法（EBB）比固定波束赋形算法（GOB）增益高1—3dB左右。     

Note on the design of linear systems with piecewise constant feedback gains

The purpose of this correspondence is to report some relations that can be used to extend the scope of a computational algorithm for the suboptimal design of linear regulator systems. This algorithm was presented in [1], and can be used to determine suboptimal piecewise constant feedback gains to be used instead of the optimal continuously time-varying ones [3]. This correspondence deals with the extension to the case where, in addition to the piecewise constant gains, the switching times are also optimized.     

Modified transpose Jacobian control of robotic systems

The simplicity of Transpose Jacobian (TJ) control is a significant characteristic of this algorithm for controlling robotic manipulators. Nevertheless, a poor performance may result in tracking of fast trajectories, since it is not dynamics-based. Use of high gains can deteriorate performance seriously in the presence of feedback measurement noise. Another drawback is that there is no prescribed method of selecting its control gains. In this paper, based on feedback linearization approach a Modified TJ (MTJ) algorithm is presented which employs stored data of the control command in the previous time step, as a learning tool to yield improved performance. The gains of this new algorithm can be selected systematically, and do not need to be large, hence the noise rejection characteristics of the algorithm are improved. Based on Lyapunov's theorems, it is shown that both the standard and the MTJ algorithms are asymptotically stable. Analysis of the required computational effort reveals the efficiency of the proposed MTJ law compared to the Model-based algorithms. Simulation results are presented which compare tracking performance of the MTJ algorithm to that of the TJ and Model-Based algorithms in various tasks. Results of these simulations show that performance of the new MTJ algorithm is comparable to that of Computed Torque algorithms, without requiring a priori knowledge of plant dynamics, and with reduced computational burden. Therefore, the proposed algorithm is well suited to most industrial applications where simple efficient algorithms are more appropriate than complicated theoretical ones with massive computational burden.     

基于信道分析的动态可重构MIMO检测器

在多输入多输出(MIMO)系统中分析信道增益对信号检测性能的影响,提出一种基于信道分析的智能MIMO信号检测算法,根据各对天线间的信道增益动态调整子节点扩展数,在检测性能接近最大似然算法性能的前提下,该算法比传统K-best算法减少54%的子节点扩展数。基于该算法设计并实现的动态可重构的超大规模集成电路MIMO检测器支持4×4天线阵列、QPSK/16-QAM调制信号,数据吞吐率达到800 Mb/s。     

一种单位代价收益决策树剪枝算法

目前关于决策树剪枝优化方面的研究主要集中于预剪枝和后剪枝算法。然而,这些剪枝算法通常作用于传统的决策树分类算法,在代价敏感学习与剪枝优化算法相结合方面还没有较好的研究成果。基于经济学中的效益成本分析理论,提出代价收益矩阵及单位代价收益等相关概念,采用单位代价收益最大化原则对决策树叶节点的类标号进行分配,并通过与预剪枝策略相结合,设计一种新型的决策树剪枝算法。通过对生成的决策树进行单位代价收益剪枝,使其具有代价敏感性,能够很好地解决实际问题。实验结果表明,该算法能生成较小规模的决策树,且与REP、EBP算法相比具有较好的分类效果。     

Estimation under unknown correlation: covariance intersection revisited

Addresses the problem of obtaining a consistent estimate (or upper bound) of the covariance matrix when combining two quantities with unknown correlation. The combination is defined linearly with two gains. When the gains are chosen a priori, a family of consistent estimates is presented in the note. The member in this family having minimal trace is said to be "family-optimal." When the gains are to be optimized in order to achieve minimal trace of the family-optimal estimate of the covariance matrix, it is proved that the global optimal solution is actually given by the covariance intersection algorithm, which conducts the search only along a one-dimensional curve in the n-squared-dimensional space of combination gains.     

On the design of linear systems with piecewise-constant feedback gains

This paper presents the theory and the computational algorithm required to determine the piecewise-constant feedback gains for the optimal control of a linear system with quadratic performance index over a finite control interval[t_{0} T]. A third order example illustrates the relation of the piecewise-constant gains to the truly optimal time-varying gains.     

An intelligent coordinator design for GCSC and AGC in a two-area hybrid power system

This study addresses the design procedure of an optimized fuzzy fine-tuning (OFFT) approach as an intelligent coordinator for gate controlled series capacitors (GCSC) and automatic generation control (AGC) in hybrid multi-area power system. To do so, a detailed mathematical formulation for the participation of GCSC in tie-line power flow exchange is presented. The proposed OFFT approach is intended for valid adjustment of proportional–integral controller gains in GCSC structure and integral gain of secondary control loop in the AGC structure. Unlike the conventional classic controllers with constant gains that are generally designed for fixed operating conditions, the outlined approach demonstrates robust performance in load disturbances with adapting the gains of classic controllers. The parameters are adjusted in an online manner via the fuzzy logic method in which the sine cosine algorithm subjoined to optimize the fuzzy logic. To prove the scalability of the proposed approach, the design has also been implemented on a hybrid interconnected two-area power system with nonlinearity effect of governor dead band and generation rate constraint. Success of the proposed OFFT approach is established in three scenarios by comparing the dynamic performance of concerned power system with several optimization algorithms including artificial bee colony algorithm, genetic algorithm, improved particle swarm optimization algorithm, ant colony optimization algorithm and sine cosine algorithm.     

Adaptive power control for wireless networks using multiple controllers and switching

Controlling transmitted power in a wireless network is critical for maintaining quality of service, maximizing channel utilization and minimizing near-far effect for suboptimal receivers. In this paper, a general proportional-integral-derivative (PID) type algorithm for controlling transmitted powers in wireless networks is studied and a systematic way to adapt or tune the parameters of the controller in a distributed fashion is suggested. The proposed algorithm utilizes multiple candidate PID gains. Depending on the prevailing channel conditions, it selects an optimal PID gain from the candidate gain set at each instant and places it in the feedback loop. The algorithm is data driven and can distinguish between stabilizing and destabilizing controller gains as well as rank the stabilizing controllers based on their performance. Simulation results indicate that the proposed scheme performs better than several candidate controllers, including a well known distributed power control (DPC) algorithm.     

Analysis of distributed power control under constant and time-varying delays

This work studies the distributed power control algorithm proposed in 1993 by Foschini-Miljanic, standardised for universal mobile telecommunication systems. Continuous and discrete time versions of this algorithm are analysed. First, the stability of the distributed power allocation schemes was studied, where sufficient conditions to guarantee stability and convergence to a desired quality of service were provided. In this study, the channel gains are assumed to be slowly time-varying or piece-wise constant. For closed-loop control, a proportional controller is then employed under integral action in order to achieve good tracking despite time-varying and unknown channel gains. Next, the effects of constant and time-varying time delays in the closed-loop structure are studied. Explicit stability regions for the control gains in the Foschini-Miljanic scheme are derived for both the continuous and discrete-time versions of the algorithm, under constant and time-varying delays. For time-varying scenario, the resulting stability regions do not impose limitations on the rate change of the time-varying profiles. A comprehensive evaluation using simulations is performed to validate the analytical derivations described in the paper.     

Variable high-gain disturbance observer design with online adaption of observer gains embedded in numerical integration

In this paper, a variable gain design approach for the high-gain disturbance observer, called Proportional-Integral-Observer (PI-Observer), is proposed to solve the problem of choosing suitable observer gains. The high-gain PI-Observer is successfully applied to estimate unknown inputs of systems together with the system states. It is known that reasonable estimations of unknown inputs can only be derived using high observer gains. On the other hand, extremely large gains will cause serious problems with respect to measurements noise and unmodeled dynamics. According to the analysis of the estimation quality regarding to the factors which influence the estimation results, the optimal level of observer gains is changing during the estimation, an online adaption for the observer gains is therefore developed. The designed PI-Observer, called Advanced PI-Observer (API-Observer), will use changing observer gains from the adaption algorithm, which is proved to give stable estimation error dynamics. Simulation results from an elastic beam example are shown to illustrate the implementation of the API-Observer.     

Optimal <Emphasis Type="Italic">N</Emphasis>-Parametric Type Iterative Learning Control

One of the most important problems in the field of the iterative learning control (ILC) is to design algorithms, in order to achieve a desired convergence rate. In this paper a new type of the ILC algorithm is introduced, which is called N-parametric type ILC with optimal gains. The convergence of the proposed algorithm is analyzed and an optimal design method is presented to determine its gains. The effect of the number of the parameters on the convergence rate of the presented ILC is investigated. It is shown that N parametric type of this ILC has a better performance than the N-1 one. Illustrative simulation examples are given to verify the theoretical analysis.     

具有后向基音检测的8 Kbit/s LD-aCELP语音编码

以降低码率为目的对G.728算法进行改进,提出了一个延迟为2.5 ms的8 Kbit/s的语音编码算法。算法引入了由最近的历史激励构成的自适应码书和归一化的固定码书的双码书结构。计算增益真值并量化,增益量化时对自适应码书用固定量化,固定码书用自适应量化。码书搜索时先进行后向基音检测,在基音周期T附近对自适应码书进行精细搜索。搜索64个自适应码矢、256个固定码矢和各自8个增益值获得最佳激励,每帧耗费20 bit。用平均分段信噪比和感知语音质量评价（PESQ）测试,改进算法编码质量接近于G.728。